Psychological distress of adolescent and young adult childhood cancer survivors in a South African cohort
Bibliographic record
Abstract
BACKGROUND: We investigated psychological distress in a South African childhood cancer survivor (CCS) cohort. METHODS: Adult CCSs treated at Tygerberg Hospital, Cape Town, completed the Brief Symptom Inventory-18. Internal consistency was acceptable: Cronbach's alpha values were 0.91 (Global Severity Index (GSI)), 0.85 (depression), 0.83 (somatization), and 0.75 (anxiety). We compared results utilizing different case rules (GSI T scores of ≥50, ≥57, and ≥63) for the identification of psychological distress. RESULTS: Forty CCSs (median age 24 years; median follow-up period 16 years) participated. Most (58%; 23 out of 40) completed school or tertiary education, were unmarried (90%; 36 out of 40), and unemployed (59.5%; 22 out of 37). The diagnoses included hematological malignancies (65%; 26 out of 40) and solid tumors (35%; 14 out of 40). The GSI T scores of ≥63, ≥57, and ≥50 identified 10% (four out of 40), 32.5% (13 out of 40), and 45% (18 out of 40) of survivors with psychological distress, respectively. Radiotherapy (odds ratio (OR) 4.6; p = .035), presence of ≥six late effects (OR 7.5; p = .026), and severe late effects (OR 6.6; p = .024) were significant risk factors (GSI T score ≥57). Follow-up period of 11-20 years (OR 7.3; p = .034) was significant for a GSI T score ≥50. CONCLUSION: This South African CCS cohort had higher levels of psychological distress utilizing the GSI T score ≥50 and ≥57 case rules than reported in the literature. Most were unmarried or unemployed. Significant contributing factors were radiotherapy, number and severity of late effects, and follow-up period. CCSs must be screened for psychological distress.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".